Identification of Smith–Magenis syndrome cases through an experimental evaluation of machine learning methods

Identification of Smith–Magenis syndrome cases through an experimental evaluation of machine learning methods

This research work introduces a novel, nonintrusive method for the automatic identification of Smith–Magenis syndrome, traditionally studied through genetic markers. The method utilizes cepstral peak prominence and various machine learning techniques, relying on a single metric computed by the resea...

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Bibliographic Details
Main Authors: Raúl Fernández-Ruiz, Esther Núñez-Vidal, Irene Hidalgo-delaguía, Elena Garayzábal-Heinze, Agustín Álvarez-Marquina, Rafael Martínez-Olalla, Daniel Palacios-Alonso
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-03-01
Series:Frontiers in Computational Neuroscience
Subjects:
Smith–Magenis syndrome
machine learning
cepstral peak prominence
acoustics
children
Online Access:https://www.frontiersin.org/articles/10.3389/fncom.2024.1357607/full

Internet

https://www.frontiersin.org/articles/10.3389/fncom.2024.1357607/full

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